Himadri Nath Saha¹, Dipanwita Chakraborty Bhattacharya^2,*, Sancharita Dutta³, Arnab Bera³, Srutorshi Basuray⁴, Satyasaran Changdar⁵, Saptarshi Banerjee⁶, Jon Turdiev⁷

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-56, 2026, DOI:10.32604/cmc.2025.070507 - 09 December 2025

Abstract The emergence of Medical Large Language Models has significantly transformed healthcare. Medical Large Language Models (Med-LLMs) serve as transformative tools that enhance clinical practice through applications in decision support, documentation, and diagnostics. This evaluation examines the performance of leading Med-LLMs, including GPT-4Med, Med-PaLM, MEDITRON, PubMedGPT, and MedAlpaca, across diverse medical datasets. It provides graphical comparisons of their effectiveness in distinct healthcare domains. The study introduces a domain-specific categorization system that aligns these models with optimal applications in clinical decision-making, documentation, drug discovery, research, patient interaction, and public health. The paper addresses deployment challenges of Medical-LLMs, More >

Khulud Salem Alshudukhi^1,*, Sijjad Ali², Mamoona Humayun^3,*, Omar Alruwaili⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.3, pp. 3029-3085, 2025, DOI:10.32604/cmes.2025.073705 - 23 December 2025

Abstract Problem: The integration of Artificial Intelligence (AI) into cybersecurity, while enhancing threat detection, is hampered by the “black box” nature of complex models, eroding trust, accountability, and regulatory compliance. Explainable AI (XAI) aims to resolve this opacity but introduces a critical new vulnerability: the adversarial exploitation of model explanations themselves. Gap: Current research lacks a comprehensive synthesis of this dual role of XAI in cybersecurity—as both a tool for transparency and a potential attack vector. There is a pressing need to systematically analyze the trade-offs between interpretability and security, evaluate defense mechanisms, and outline a… More >

Puranam Revanth Kumar¹, Gouse Baig Mohammad², Pallati Narsimhulu³, Dharnisha Narasappa⁴, Lakshmana Phaneendra Maguluri⁵, Subhav Singh^6,7,8, Shitharth Selvarajan^9,10,11,*

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.3, pp. 2779-2818, 2025, DOI:10.32604/cmes.2025.066365 - 30 September 2025

Abstract Blockchain Technology (BT) has emerged as a transformative solution for improving the efficacy, security, and transparency of supply chain intelligence. Traditional Supply Chain Management (SCM) systems frequently have problems such as data silos, a lack of visibility in real time, fraudulent activities, and inefficiencies in tracking and traceability. Blockchain’s decentralized and irreversible ledger offers a solid foundation for dealing with these issues; it facilitates trust, security, and the sharing of data in real-time among all parties involved. Through an examination of critical technologies, methodology, and applications, this paper delves deeply into computer modeling based-blockchain framework… More >

Isha Kiran¹, Shahzad Ali^2,3, Sajawal ur Rehman Khan^4,5, Musaed Alhussein⁶, Sheraz Aslam^7,8,*, Khursheed Aurangzeb^6,*

CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 5057-5078, 2025, DOI:10.32604/cmc.2025.058724 - 06 March 2025

Abstract Cardiovascular disease (CVD) remains a leading global health challenge due to its high mortality rate and the complexity of early diagnosis, driven by risk factors such as hypertension, high cholesterol, and irregular pulse rates. Traditional diagnostic methods often struggle with the nuanced interplay of these risk factors, making early detection difficult. In this research, we propose a novel artificial intelligence-enabled (AI-enabled) framework for CVD risk prediction that integrates machine learning (ML) with eXplainable AI (XAI) to provide both high-accuracy predictions and transparent, interpretable insights. Compared to existing studies that typically focus on either optimizing ML… More >

Wenqing Xiao^1,#, Lewen Tan^1,2,#, Xunjun Chen^1,*, Qiaoguang Li^1,*, Yunqing Ruan¹

Journal of Renewable Materials, Vol.12, No.3, pp. 569-583, 2024, DOI:10.32604/jrm.2023.046662 - 11 April 2024

Abstract

In this article, a series of high refractive indices (1.50–1.53) thiol phenyl polysiloxane (TPS) were synthesized via hydrolytic sol–gel reaction. The Fourier transform infrared spectra (FT–IR) and nuclear magnetic resonance spectra (NMR) results showed that TPS conformed to the predicted structures. Natural terpene linalool was exploited as photocrosslinker to fabricate UV–curing linalool–polysiloxane hybrid films (LPH) with TPS via photoinitiated thiol–ene reaction. LPH rapidly cured under UV irradiation at the intensity of 80 mW/cm² in 30 s, exhibiting good UV–curing properties. The optical transmittance of LPH in the wavelength of 300–800 nm was over 90%, exhibiting good

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Chaodong Liu, Yutong Yang, Boyu Cui, Weihong Wang^*

Journal of Renewable Materials, Vol.10, No.11, pp. 2961-2972, 2022, DOI:10.32604/jrm.2022.019761 - 29 June 2022

Abstract Microfibrillated cellulose (MFC) is often added to polylactic acid (PLA) matrixes as a reinforcing filler to obtain fully-biodegradable composites with improved mechanical properties. However, the incompatibility between MFC and the PLA matrix limits the mechanical performance of MFC-reinforced PLA composites. In this paper, DL-lactic acid-grafted-MFC (MFC-g-DL) was used to improve the compatibility with PLA. Reinforced composites were prepared by melt extrusion and hot-cold pressing. The tensile strength of the PLA/MFC-g-DL composite increased by 22.1% compared with that of PLA after adding 1% MFC-g-DL. Scanning electron microscopy (SEM), differential scanning calorimetry (DSC), and dynamic thermomechanical analysis (DMA) were… More > Graphic Abstract